Diagnosing and Monitoring CNS Malignancies Using MicroRNA

ABSTRACT

The use of specific microRNAs (miRNAs) present in CSF as biomarkers for particular brain malignancies and disease activity.

CLAIM OF PRIORITY

This application is a continuation of U.S. patent application Ser. No.13/885,762, filed May 16, 2013, which is a U.S. National PhaseApplication under 35 U.S.C. §371 of International Patent Application No.PCT/US2011/061047, filed on Nov. 16, 2011, which claims the benefit ofU.S. Provisional Patent Application Ser. No. 61/457,000, filed on Nov.16, 2010. The entire contents of the foregoing are hereby incorporatedby reference in their entireties.

FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with Government support under Grant Nos.CA023100, CA124804, and CA138734 awarded by the National Institutes ofHealth. The Government has certain rights in the invention.

TECHNICAL FIELD

The present methods relate to the use of specific microRNAs (miRNAs)that are present in CSF as biomarkers for particular brain malignanciesand disease activity.

BACKGROUND

The most frequently occurring brain malignancies in adults aremetastatic brain cancers (e.g., from primary melanoma, lung cancer,breast cancer, gastrointestinal cancer (e.g., pancreatic or colorectal),kidney cancer, bladder cancer, certain sarcomas, or testicular or germcell tumors) followed by glioblastoma (GBM). GBM is the most aggressiveprimary brain cancer, which generally has a poor prognosis with mediansurvival of about 14 months, despite aggressive treatment (Filippini etal. Neuro Oncol. 2008; 10(0:79-87). Currently diagnosis of brain tumorsis made with brain biopsy if possible and the analysis of cerebrospinalfluid (CSF) for the presence of cancer cells (cytology). CSF can beaccessed readily for longitudinal disease monitoring during and aftertherapy. However, the currently used method of CSF analysis has moderatesensitivity, is non-quantitative and technically challenging. There ispresently no routine way to subtype the malignancy and monitor molecularchanges from CSF indicating the need for more accurate and reliablebiomarkers and methods.

SUMMARY

The present invention is based on the identification of specific miRNAsthat can serve as biomarkers for particular brain malignancies anddisease activity.

Thus, in a first aspect, the invention provides methods for detecting ormaking a diagnosis between metastatic and primary brain tumors. Themethods include determining levels of miR-10b, miR-21, and miR-200 in asample from a subject, and comparing the levels of miR-10b, miR-21, andmiR-200 to reference levels of miR-10b, miR-21, and at least one miR-200family member. The presence of levels of all of miR200, miR-10b ormiR-21 below the reference levels indicates the absence of a metastaticor primary brain tumor. The presence of levels of miR-10b or miR-21above the reference levels indicates the presence of a metastatic orprimary brain tumor. The presence of levels of the miR-200 family memberabove the reference level indicates the presence of a metastatic braintumor.

In another aspect, the invention provides computer-implemented methodsfor detecting or making a diagnosis between metastatic and primary braintumors. The methods include determining levels of miR-10b, miR-21, andat least one miR-200 family member, in a sample from a subject, toprovide a subject dataset; downloading the dataset into a computersystem having a memory, an output device, and a processor programmed forexecuting an algorithm, wherein the algorithm assigns the datasets intoone of two categories levels of miR-10b, miR-21, and at least onemiR-200 family member; assigning the subject dataset into the first orsecond category; and generating an output comprising a report indicatingthe assignment to the first or second category.

In some embodiments, the first category is presence of a primary braintumor and the second category is presence of a metastatic brain tumor.In some embodiments, an assignment to the first category is made basedon the presence of levels of miR-10b or miR-21 above reference levels,and the presence of levels of the miR-200 family member below areference level; and an assignment to the second category is made basedon the presence of levels of miR-10b or miR-21 above reference levels,and the presence of levels of the miR-200 family member above thereference level.

In some embodiments, the first category is presence of a primary braintumor or a metastatic brain tumor, and the second category is absence ofa primary brain tumor or a metastatic brain tumor. In some embodiments,an assignment to the first category is made based on the presence of anyof miR200, miR-10b or miR-21 above reference levels, and an assignmentto the second category is made based on the presence of levels of all ofmiR200, miR-10b or miR-21 below the reference levels.

In some embodiments, the algorithm is a linear algorithm or radial basisfunction.

In some embodiments, the algorithm is a linear algorithm comprising:

(a*miR-125b)+(b*miR-10b)+(c*miR-21)+(d*miR-141)+(e*miR-200a)+(f*miR-200b)+(g*miR-200c)−h,wherein a-g are weights and his a constant, determined using a supportvector machine algorithm.

In some embodiments, the methods further include selecting a treatmentfor a metastatic or primary brain tumor for the subject, based on thepresence of a metastatic or primary brain tumor.

In some embodiments, the methods further include administering thetreatment to the subject.

In another aspect, the invention provides methods for monitoringprogression of a brain tumor. The methods include determining levels ofone or more of miR-10b, miR-21, and a miR-200 family member in a firstsample; and determining levels of one or more of miR-10b, miR-21, and amiR-200 family member in a subsequent sample. The presence of levels ofmiR-10b, miR-21, or miR-200 family member in the subsequence sampleabove the levels in the first sample indicates the presence ofprogression or recurrence of the brain tumor, and levels of miR-10bmiR-21, or miR-200 family member in the subsequent sample below thelevels in the first sample indicates that the brain tumor is regressingor is in remission.

In some embodiments, wherein the subject has been diagnosed with aprimary brain tumor, the methods include monitoring levels of one orboth of miR-10b and miR-21. In some embodiments, wherein the subject hasbeen diagnosed with a metastatic brain tumor, the methods includemonitoring levels of one or more of miR-10b, miR-21, and a miR-200family member.

In some embodiments, the methods further include administering atreatment to the subject, e.g., between the first and subsequentsamples, and a decrease in levels of miR-10b, miR-21, or at least onemiR-200 family member in the subsequence sample as compared to the levelin the first sample indicates that the treatment was effective, e.g.,reduced the size of the tumor. No change indicates that the treatmenteither halted tumor growth or had no effect, and an increase indicatesthat the treatment was not effective.

In some embodiments, the treatment includes administration of one ormore of surgical resection, chemotherapy, or radiotherapy.

In some embodiments of the methods described herein, the samplecomprises cerebrospinal fluid from a subject.

In some embodiments of the methods described herein, the subject is ahuman who has or is suspected of having a brain tumor.

In some embodiments of the methods described herein, the levels aredetermined using RT-PCR.

In some embodiments of the methods described herein, the miR-200 familymember is miR-200a, miR-200b, miR-200c, miR-141, or miR-429.

In some embodiments of the methods described herein, the methodcomprises normalizing the levels to a level of a housekeeping miRNA,e.g., miR-125 or miR-24.

In some embodiments of the methods described herein, the primary braintumor is a glioma, glioblastoma, hemangioma, or medulloblastoma.

In some embodiments of the methods described herein, the metastaticbrain tumor is from a primary lung, breast, kidney, bladder, testicular,germ cell or gastrointestinal cancer, or melanoma.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Methods and materials aredescribed herein for use in the present invention; other, suitablemethods and materials known in the art can also be used. The materials,methods, and examples are illustrative only and not intended to belimiting. All publications, patent applications, patents, sequences,database entries, and other references mentioned herein are incorporatedby reference in their entirety. In case of conflict, the presentspecification, including definitions, will control.

Other features and advantages of the invention will be apparent from thefollowing detailed description and figures, and from the claims.

DESCRIPTION OF DRAWINGS

FIGS. 1A-C show miR-10b and miR-21 up-regulation in GBM, and CSF levelsof miR-10b and miR-21 in patients with GBM, metastatic brain cancer andnon-neoplastic controls. (1A) miRNAs deregulated in GBM more than twofold as compared to normal brains. miRNA levels were obtained by theanalysis of TCGA miRNA microarrays data and error bars representstandard deviation between individual probe sets present for each miRNAon the arrays. (1B) miR-10b and (1C) miR-21 levels were examined byqRT-PCR in CSF samples of neurological patients, and the relative levelsare demonstrated for individual CSF samples. The lines indicate medianmiRNA levels for each group of patients: “Controls”—non-neoplasticneuropathological cases, “GBM”—glioblastoma cases, “Breast to Brain” and“Lung to Brain”—breast and lung cancer brain metastasis, “Breast LM” and“Lung LM”—breast and lung cancer leptomeningeal metastasis,respectively. Differences between group means have been determined bynon-parametric Wilcoxon Signed Rank test and the significance isindicated by asterisks: (*) p<0.05, (**) p<0.001, (***) p<0.0001.miR-10b and miR-21 CSF levels normalized to miR-125b are presented inFIGS. 6A-B.

FIGS. 2A-F show the results of detection of miRNAs of miR-200 family inmetastatic brain cancer patients. (2A) miR-200b expression levels wereexamined by qRT-PCR in various primary and metastatic brain tumor tissuespecimens and normalized to ubiquitously expressed miR-125b. Error barsindicate standard errors between technical duplicates. PNET: primitiveneuroectodermal brain tumor. MiR-200a (2B), miR-200b (2C), miR-200c (2D)and miR-141 (2E) levels were examined by qRT-PCR in CSF samples ofneurological patients, and the relative values are demonstrated forindividual patients. Differences between group means that reachedstatistical significance as determined by non-parametric Wilcoxon SignedRank test are indicated with asterisks: (*) p<0.05, (**) p<0.001, (***)p<0.0001. Corresponding values normalized to miR-125b are presented inSuppl. FIG. 2 C-F. (2F) The average levels of miR-200a/miR-200b andmiR-141/miR-200c cluster miRNAs in CSF of metastatic brain cancerpatients. The error bars represent the standard error of mean for eachgroup of patients.

FIG. 3A is an exemplary diagnostic decision tree showing a method ofclassification of brain cancer patients based on CSF miRNA biomarkers(miR-10b, -21, and -200).

FIG. 3B is a pair of graphs showing the correlation of miR-10b andmiR-21 levels between brain tumors and matching CSF samples collectedfrom the same patients. The Pearson coefficients (r) of linearregression between two data sets were calculated for each miRNA.

FIGS. 4A-C show CSF levels of miRNA markers in metastatic lung cancerand GBM patients during treatment with erlotinib. miRNAs levels wereexamined by qRT-PCR in CSF samples of lung cancer patients (Patients A,C) and GBM patient (Patient B) during the time course of erlotinibtreatment. The disease progression and the drug response wereconcomitantly monitored by MRI, as following. For Patient A (shown inFIG. 4A): serial axial post-gadolinium MRIs of lung cancer patient'sbrain during course of progression of disease and stability andimprovement on MRI with escalating doses of erlotinib. A: time 0 weekswhile patient on erlotinib, there is no leptomeningeal and parenchymalenhancement and CSF cytology was negative; B: 3 weeks progression onerlotinib 150 mg daily dosing with new cerebellar leptomeningealenhancement (small arrows) and nodule (large arrow), erlotinib increasedto 600 mg every 4 days at 9 weeks; C: 29 weeks on showing stableleptomeningeal enhancement and nodule; D-40 weeks showing reduction inleptomeningeal enhancement and nodule, erlotinib increased to 900 mgevery 4 days at 41 weeks; E: 64 weeks after 6 cycles of chemotherapywith carboplatinum and pemetrexed due to lung cancer progression showingfurther reduction in leptomeningeal enhancement and nodule hasdisappeared. For Patient B (shown in FIG. 4B): A: time 2 weeks forpatient with GBM with predominant mass effect and enhancement felt to beradiation changes rather than tumor based on MM spectroscopy and PETscan on erlotinib at 600 mg every 4 days; B: 26 weeks on treatmentshowing progression on MRI with new lesion (arrow) concerning for tumor;C: 27 weeks on treatment showing hypermetabolic area (arrow) on PETconsistent with tumor and biopsy confirmed. For Patient C (shown in FIG.4C): had inadequate treatment due to functional status and rapidlyprogressed over a few weeks, which was reflected by an increase inlevels of miR-200 family members in a short interval.

FIGS. 5A-G are graphs showing miR-NA levels in CSF of patients with GBM,metastatic brain cancers and non-neoplastic neurological conditions.miR-NA levels were determined in CSF samples by qRT-PCR and relativelevels calculated by ΔCt method with expression at Ct=36 set as oneunit.

FIGS. 6A-F are graphs showing miRNA levels in CSF of randomly selectedpatients with GBM, metastatic brain cancers and non-neoplastic controlsare demonstrated for: (6A) miR-15b, (6B) miR-15b normalized to miR-125b,(6C) miR-17-5p, (6D) miR-17-5p normalized to miR-125b, (6E) miR-93, (6F)miR-93 normalized to miR-125b. miRNA levels in CSF samples weredetermined by qRT-PCR reaction. Relative miRNA levels were quantified bythe ΔCt method and normalized to miR-125b as described in Materials andmethods. Error bars represent standard error of mean between technicalduplicates.

FIGS. 7A-B are bar graphs showing miR-10b expression in different humantissues. (7A) The normalized data on miR-10b expression in various humantissues were obtained from qRT-PCR based profiling (Liang, 2007).miR-10b levels were calculated relative to miR-10b expression in brain,which was set as one unit. (7B) The data on miR-10b expression in normalhuman tissues and corresponding carcinomas were obtained from profilingbased on hybridization arrays (Lu, 2005), Gene Expression Omnibus (GEO)accession number GSE2564. Normalized miR-10b signals were plottedrelative to miR-10b level in brain, which was set as one unit.

FIGS. 8A-B are bar graphs showing miRNA-200 family in different humantissues. (8A) The normalized data on miR-200a, -200b, 200c and miR-141expression in human tissues were obtained from qRT-PCR based profiling(Liang, 2007). miRNA levels were calculated relative to correspondingmiRNA expression levels in brain, which were set as one unit. (8B) Thedata on miR-200 family expression in normal human tissues andcorresponding carcinomas were obtained from profiling based onhybridization arrays (Lu, 2005); Gene Expression Omnibus (GEO) accessionnumber GSE2564. Normalized signals for specific miRNAs were plottedrelative to corresponding miRNA levels in brain, which were set as oneunit.

FIG. 9. miR-195 levels in CSF of randomly selected patients with GBM,metastatic brain cancers and non-neoplastic controls. miR-195 levels inCSF samples were determined by qRT-PCR reaction. Relative miRNA levelswere quantified by ΔCt method as described. Error bars representstandard error of mean between technical duplicates.

FIGS. 10A-F are graphs showing miRNA levels in CSF of patients with GBMand metastatic brain cancers remissions. The levels of (10A) miR-10b,(10B) miR-21, (10C) miR-200a, (10D) miR-200b, (10E) miR-200c and (10F)miR-141 were determined in CSF by qRT-PCR reaction. Relative miRNAlevels were quantified by ΔCt method and normalized to miR-125b asdescribed in Materials and methods. Average miRNA levels are presentedfor each group of cancer patients and individual miRNA levels arepresented for patients with cancer remissions. Error bars representstandard error of mean within groups of patients.

DETAILED DESCRIPTION

miRNAs are small endogenous mediators of RNA interference and keyregulatory components of many biological processes required for organismdevelopment, cell specialization and homeostasis. Many miRNAs exhibittissue-specific patterns of expression and are deregulated in variouscancers, where they can either be oncogenic (oncomirs) or tumorsuppressive. The recent discovery of miRNAs in the secreted membranevesicles, exosomes^(2, 3), as well as in the blood serum^(4, 5) andother body fluids⁶ suggested that miRNAs play a role in intercellularcommunication in both paracrine and endocrine manner. It had also openeda new exciting direction for study of miRNAs as biomarkers for diseases,and cancer diagnostics by miRNA profile in blood serum became a quicklygrowing field⁷.

Several studies have reported miRNA detection, among several biologicalfluids, in CSF⁸⁻¹⁰, raising the possibility that miRNAs in CSF mightserve as informative biomarkers of central nervous system (CNS) disease.Such a possibility, largely unexplored until now, is supported by thefinding that different types of brain cancer have distinct signatures ofmiRNA expression, with some miRNAs species abundant in cancer whileundetectable in healthy brain¹¹⁻¹³ Since CSF is separated from bloodcirculation by blood-brain barrier, it is conceivable that CSF mightbetter retain a unique signature of miRNA expression specific for braintumors.

A recent study demonstrated the usefulness of miRNA profiling in CSF fordiagnostics of brain lymphoma¹⁰. In the current study, levels of severalcandidate miRNAs were tested in the CSF of patients with GBM andcompared to those of metastatic brain cancers and a variety ofnon-neoplastic CNS diseases. There was a strong association between theparticular types of brain cancer and the presence of specific miRNAs inCSF. Using this approach enables detection of GBM and metastatic braincancers, and discrimination between them with about 95% accuracy. Theseresults demonstrate the utility of miRNA as biomarkers of high-gradebrain malignancies and reveal their value for the development ofdiagnostic and prognostic tools, as well as for monitoring of CNSpathology in general.

Methods of Diagnosis/Detection of CNS Malignancies

Thus, the methods described herein can be used to diagnose, i.e., detectthe presence of, a CNS malignancy, based on levels of miRNAs in CSF,e.g., levels of one or more of miR-21, miR-10b, and or a miR-200 (asused herein, the term “miR-200” encompasses all members of the miR-200family, i.e., miR-200a, miR-200b, miR-200c, miR-141, and miR-429). Insome embodiments, levels of miR-10b are determined and compared to areference level, and the presence of levels of miR-10b in the subjectabove the reference level indicates that the subject has a metastatic orneoplastic primary brain tumor, e.g., GBM. In some embodiments, levelsof miR-200 are determined and compared to a reference level, and thepresence of levels of miR-200 (e.g., miR-200a) in the subject above thereference level indicates that the subject has a metastatic brain tumor,e.g., from primary lung or breast cancer. In some embodiments, levels ofmiR-21 are determined and compared to a reference level, and thepresence of levels of miR-21 in the subject above the reference levelindicates that the subject has a metastatic or neoplastic primary braintumor, e.g., GBM. In some embodiments, the methods include determininglevels of miR-10b or miR-21 and miR-200 (either non-normalized ornormalized to relatively uniformly expressed miRNAs such as miR-125 ormiR-24), and comparing the levels of each miRNA to a reference level. Inthis case, the presence of elevated miR-10b or miR-21 indicates thepresence of a metastatic or neoplastic primary brain tumor, e.g., GBM,and the presence of miR-200 indicates the presence of a metastatic braintumor. See, e.g., FIG. 3A.

In some embodiments, the methods are used to determine whether ametastatic brain tumor originated from a primary breast or lung tumor.The methods include detecting levels of miR-200a and/or miR-200b. Thepresence of increased levels of miR-200a and miR-200b (two miRNAsencoded as a cluster at chromosome 1p36.33) in CSF indicate an increasedlikelihood of the presence of metastatic breast cancer relative to lungcancer. In some embodiments, the methods include determining CSF levelsof miR-141 and -200c (co-encoded at chromosome 12p13.31), which areexpressed at similar levels in breast and lung cancer cases, anddetermining a ratio between the miRNAs of the two different miR-200genomic clusters (e.g., [level of miR200a+level of miR200b]/[level ofmiR141+miR200c], wherein a ratio above a reference ratio indicates anincreased likelihood of the presence of metastatic breast cancerrelative to lung cancer.

In some embodiments, the methods are used to make a differentialdiagnosis of GBM versus brain metastasis, or GBM and brain metastasisversus non-neoplastic tumors on the basis of detection of levels in aCSF sample of seven miRNAs: miR-10b, miR-21, miR-125b, miR-141,miR-200a, miR-200b, and miR-200c as independent variables. Each of thesemiRNAs is detected in the sample, and an algorithm (e.g., a linear orradial is applied to make a diagnosis.

Reference levels can be determined using methods known in the art, e.g.,standard epidemiological and biostatistical methods. The reference levelcan represent the levels in a reference cohort, e.g., levels in subjectswho do not have GBM or metastatic brain cancer. The reference levels canbe, e.g., median levels, or levels representing a cutoff for the highestquartile, and can be set to provide sufficient specificity and accuracyto provide for an optimal level of true positives/true negatives whileminimizing levels of false positives/false negatives. Appropriatemethods are known in the art. See, e.g., Fleiss, “Design and Analysis ofClinical Experiments,” (Wiley-Interscience; 1 edition (Feb. 22, 1999));Lu and Fang, “Advanced Medical Statistics,” (World Scientific Pub Co Inc(Mar. 14, 2003)); Armitage et al., “Statistical Methods in MedicalResearch, 4^(th) Ed”, Blackwell Science (Boston, Mass., Oxford:Blackwell Scientific Publications, 2001).

In some embodiments, the methods include determining levels of miR-125b,and normalizing levels of other miRNAs to the levels of miR-125b, see,e.g., FIGS. 5A-5G. The reference levels can then be set in comparison tothose normalized levels, using methods known in the art.

In some embodiments, miRNA levels are determined after an initialdiagnosis of a brain mass, e.g., detection of a mass using an imagingmethod such as MM, or after a subject has presented with symptoms thatare consistent with a brain mass, to assist in making a differentialdiagnosis of GBM versus brain metastasis versus non-neoplastic tumor. Ahealth care provider can identify subjects who have symptoms consistentwith a brain mass based on knowledge in the art; general signs andsymptoms include new onset or change in pattern of headaches; headachesthat gradually become more frequent and more severe; unexplained nauseaor vomiting; vision problems, such as blurred vision, double vision orloss of peripheral vision; gradual loss of sensation or movement in anarm or a leg; difficulty with balance; speech difficulties; confusion ineveryday matters; personality or behavior changes; seizures, especiallyin someone who doesn't have a history of seizures; and hearing problems.

In some embodiments, once a differential diagnosis is made, the methodsinclude the selection and optionally the administration of a treatmentfor the diagnosed disease. Thus, the methods can include selecting atreatment regimen for the subjects comprising one or more of surgicalintervention, chemotherapy, and radiotherapy. For all brain cancers, thechoice of therapy (e.g., surgery, radiation and/or chemotherapy) can bechosen depending on site, size, neurological function, and systemicdisease status. For example, if the subject has GBM, then a treatmentregime including radiation, temozolamide, and avastin may be selectedand optionally administered. If the subject has metastatic brain cancer,then the treatment may depend on the source of the metastasis, i.e., onthe primary cancer. For metastatic breast cancer, then the treatmentcould include chemotherapies approved for breast cancer (e.g.,herceptin, lapatinib, doxil, or taxanes); for lung metastases, then lungcancer therapies can be selected (e.g., tarceva, alimta, orcarboplatin). One of skill in the art would be able to select anappropriate treatment based on knowledge in the art. See, e.g., theNational Comprehensive Cancer Network (NCCN) Guidelines, available onthe internet at nccn.org.

For a subject who has been determined to have a non-neoplastic lesionusing a method described herein, the methods can include monitoring thesubject on a continuing basis to detect any change in the lesion, e.g.,a shift to malignancy, which would be indicated by an increase in levelsof miR-10b, miR-21, or miR-200.

Methods of Monitoring CNS Malignancies

The methods described herein can also be used to monitor a subject,e.g., a subject who is undergoing treatment or being followed forprogression. The methods include determining levels of miR-10b, miR-21,and/or miR-200, wherein the presence of levels of miR-10b, miR-21, ormiR-200 above a reference level indicate the presence of recurrence ofthe malignancy, and levels below the reference level indicate that thesubject is in remission.

In some embodiments, e.g., for a subject who is undergoing treatment,levels of miR-10b, miR-21, and/or miR-200 can be monitored over time(e.g., by comparing levels determined from first and second, e.g.,subsequent, samples taken over time; the first sample can be, but neednot be, a baseline or initial sample); a decrease in levels of miR-10b,miR-21, and/or miR-200 in a subject undergoing treatment indicates thatthe treatment is effective. An increase in levels indicates progression.No significant change in levels indicates that no significant change hasoccurred, i.e., no significant change in a subject being treated thatthe treatment is at best slowing growth of the tumor, or is ineffective,and no significant change in a subject who is not being treatedindicates that the tumor is not progressing. The presence of elevatedlevels in a subject who was previously in remission indicates thepresence of a recurrence of the tumor, and can indicate a need fortreatment.

In addition, the methods can be used to detect real progression versuspseudoprogression (a phenomenon in which a subject is observed to haveexperienced disease growth immediately after therapy, e.g., afterradiotherapy, but are later shown to have improved or stable disease bybrain imaging, see, e.g., Hoffman et al., J Neurosurg 50:624-628, 1979;Brandes et al., Clin Oncol 26:2192-2197, 2008; de Witt et al., Neurology63:535-537, 2004; Taal et al., Cancer 113:405-410, 2008), e.g., insubjects with GBM. In the case of an apparent progression (e.g., asmeasured by imaging), the presence of stable or decreasing levels ofmiR-10b (or miR-200) as compared to earlier levels (e.g., pre-treatmentlevels) indicates that the apparent progression is a pseudoprogression.

The levels can be determined, e.g., before, during, or after treatment,e.g., treatment with surgery (e.g., resection or debulking),chemotherapy, or radiotherapy.

Methods of Detection

Any methods known in the art can be used to detect and/or quantifylevels of a miRNA as described herein. For example, the level of a miRNAcan be evaluated using methods known in the art, e.g., RT-PCR (e.g., theTAQMAN miRNA assay or similar), quantitative real time polymerase chainreaction (qRT-PCR), Northern blotting, RNA in situ hybridization(RNA-ISH), RNA expression assays, e.g., microarray analysis, deepsequencing, cloning or molecular barcoding (e.g., NANOSTRING, asdescribed in U.S. Pat. No. 7,473,767). Analytical techniques todetermine miRNA levels are known. See, e.g., Sambrook et al., MolecularCloning: A Laboratory Manual, 3rd Ed., Cold Spring Harbor Press, ColdSpring Harbor, N.Y. (2001).

In some embodiments, the methods include contacting an agent thatselectively binds to a biomarker, e.g., to a miRNA (such as anoligonucleotide probe that binds specifically to the miRNA) with asample, to evaluate the level of the miRNA in the sample. In someembodiments, the agent bears a detectable label. The term “labeled,”with regard to an agent encompasses direct labeling of the agent bycoupling (i.e., physically linking) a detectable substance to the agent,as well as indirect labeling of the agent by reactivity with adetectable substance. Examples of detectable substances are known in theart and include chemiluminescent, fluorescent, radioactive, orcolorimetric labels. For example, detectable substances can includevarious enzymes, prosthetic groups, fluorescent materials, luminescentmaterials, bioluminescent materials, and radioactive materials. Examplesof suitable enzymes include horseradish peroxidase, alkalinephosphatase, beta-galactosidase, or acetylcholinesterase; examples ofsuitable prosthetic group complexes include streptavidin/biotin andavidin/biotin; examples of suitable fluorescent materials includeumbelliferone, fluorescein, fluorescein isothiocyanate, rhodamine,dichlorotriazinylamine fluorescein, dansyl chloride, quantum dots, orphycoerythrin; an example of a luminescent material includes luminol;examples of bioluminescent materials include luciferase, luciferin, andaequorin, and examples of suitable radioactive material include ¹²⁵I,¹³¹I, ³⁵S or ³H.

In some embodiments, high throughput methods, e.g., arrays (e.g., TAQMANArray MicroRNA Cards) or gene chips as are known in the art (see, e.g.,Ch. 12, “Genomics,” in Griffiths et al., Eds. Modern genetic Analysis,1999, W. H. Freeman and Company; Ekins and Chu, Trends in Biotechnology,1999; 17:217-218; MacBeath and Schreiber, Science 2000,289(5485):1760-1763; Hardiman, Microarrays Methods and Applications:Nuts & Bolts, DNA Press, 2003), can be used to detect the presenceand/or level of a miRNA.

In some embodiments, the methods include using a modified RNA in situhybridization technique using a branched-chain DNA assay to directlydetect and evaluate the level of a miRNA in the sample (see, e.g., Luoet al., U.S. Pat. No. 7,803,541B2, 2010; Canales et al., NatureBiotechnology 24(9):1115-1122 (2006); Nguyen et al., Single Molecule insitu Detection and Direct Quantiication of miRNA in Cells and FFPETissues, poster available at panomics.com/index.php?id=product_87). Akit for performing this assay is commercially-available from Affymctrix(VicwRNA).

Human miRNA Sequences

The following table sets forth sequences for mature human miRNAs usefulin the present methods.

SEQ ID Micro RNA NO: Mature Sequence miR-10b  1 UACCCUGUAGAACCGAAUUUGUGmiR-21  2 UAGCUUAUCAGACUGAUGUUGA miR-24-1  3 UGCCUACUGAGCUGAUAUCAGUmiR-24-2  4 UGCCUACUGAGCUGAAACACAG miR-200a  5 CAUCUUACCGGACAGUGCUGGAmiR-200b  6 CAUCUUACUGGGCAGCAUUGGA miR-200c  7 CGUCUUACCCAGCAGUGUUUGGmiR-141  8 CAUCUUCCAGUACAGUGUUGGA miR-429  9 UAAUACUGUCUGGUAAAACCGUmiR-125 10 UCCCUGAGACCCUAACUUGUGA

Algorithms and Computer-Implemented Methods

In some embodiments, the methods include using one or more algorithms toassign a diagnosis, based on levels of miRNAs as described herein. Forexample, the methods can include the use of a linear algorithm, in whichone or more of the levels are weighted. In another example, the methodscan include the use of a radial basis function (RBF). Appropriate linearand RBF algorithms useful in the present methods can be generated usingmethods known in the art, e.g., a support vector machine (SVM). The SVMwas originally developed by Boser, Guyon and Vapnik (“A trainingalgorithm for optimal margin classifiers”, Fifth Annual Workshop onComputational Learning Theory, Pittsburgh, ACM (1992) pp. 142-152). See,e.g., Vapnik, “Statistical Learning Theory.” John Wiley & Sons, Inc.1998; Cristianini and Shawc-Taylor, “An Introduction to Support VectorMachines and other kernel-based learning methods.” Cambridge UniversityPress, 2000. ISBN 0-521-78019-5; and Schölkopf and Smola, “Learning withKernels.” MIT Press, Cambridge, Mass., 2002, as well as U.S. Pat. Nos.7,475,048 and 6,882,990, all of which are incorporated herein byreference in their entirety for their teachings relating to computersystems and SVM-based methods. For example, the present methods can beperformed using a computer system as described in FIG. 4 of U.S. Pat.No. 7,475,048.

EXAMPLES

The invention is further described in the following examples, which donot limit the scope of the invention described in the claims.

Materials and Methods

The following materials and methods were used in Examples 1-5, below.

Collection of Samples.

CSF and brain tumor samples were obtained from the Department ofNeurosciences, UC San Diego, Moores Cancer Center, La Jolla, Calif.,Department for Neurosurgery at Brigham and Women's Hospital, Boston,Mass., and from the Department for Neurosurgery at Gottingen UniversityMedical Center, Gottingen, Germany over the period of 2-5 years. Atleast one ml of each CSF sample was cleared of cells and debrisimmediately after collection by brief centrifugation at 3000 rpm 5 minat 4° C. and stored in aliquots at −80° C. All tumor specimens werefresh-frozen on dry ice and stored at −80° C. until tested.

RNA Isolation and miRNA Profiling.

CSF samples were lyophilized and total RNA was extracted using mirVanamiRNA isolation kit (Ambion) according to the manufacturer's protocol.The amount of RNA extracted from the CSF samples was within 50-2500ng/ml range, consistent with the previous findings³. Total RNA fromfrozen tumor tissues was isolated using Trizol reagent (Invitrogen). Thelevels of individual miRNAs in CSF and tumors were determined by TaqManmiRNA assays from Applied Biosystems. Four ng of total RNA was used in 6μl of reverse transcription reaction with specific miRNA RT probes,prior to TaqMan real-time PCR reactions that were performed induplicates. MiR-125b, which is abundantly and uniformly expressed inbrain, was detected in all CSF samples and used as an internal controlfor normalization (FIG. 5). However, since miR-125b levels themselvesare not uniform across the CSF samples, both normalized andnon-normalized data were considered in this study. No miRNA marker thatwas less variable across the CSF samples was identifiable, and generallyhigher miRNA CSF levels were observed in neoplastic cases relative tonon-neoplastic controls. This trend may reflect a release ofmiRNA-containing microvesicles by cancer cells³ and/or destruction ofthe brain tissue in neoplastic conditions. miRNAs levels were calculatedrelative to corresponding miR-125b levels by the formula 2̂ΔCt, whereΔCt=Ct_(miR-125b)-Ct_(miR-x). All data are mean of technical duplicates,and the standard errors of mean were calculated between duplicates.Normalization to another housekeeping miRNA, miR-24, did not change theresults (data not shown).

Samples Classification and Data Analysis.

A total of 118 patients of two neurooncological clinics, andcorresponding CSF samples were analyzed in this study. 108 patients wereclassified into six groups based on clinical and pathological diagnoses(including CSF cytology and tumor histology when applicable), andmagnetic resonance imaging (MRI) findings (Table 1A, the detailedpatients' characteristics are listed in Table 1B). The first controlgroup referred as “Non-neoplastic” includes patients with variousneurological conditions other than brain neoplasia. The patients in thisgroup had no cancer at the time of CSF collection, and no previoushistory of CNS malignancies. The second group “GBM” includes patientsdiagnosed with active GBM. GBM was referred to as clinically “active”when primary tumor mass was apparent by MRI imaging at the time of CSFsamples collection and was further classified as GBM by tumor tissuehistology. The two groups called “Breast to Brain” and “Lung to Brain”comprise of samples from the patients with parenchymal brain metastasisfrom breast carcinoma and lung cancer (including SCLC and NSCLC),respectively. The presence of metastases in these patients was confirmedby MRI imaging at the time of CSF collection. Two additional groupsrepresent patients with documented leptomeningial metastasis of thesecancers (CSF or MRI positive disease). Additional seven patients notincluded in the groups described above were analyzed separately. Thesepatients represent cases of remission of primary and metastatic braintumors, as indicated by no detectable brain tumor at the time of CSFcollection based on imaging features, clinical stability and CSFcytology. The remaining three patients were analyzed in the longitudinalstudy.

TABLE 1A Groups of patients included in this study Group NClinical/Pathology based diagnosis Control 15 Non-neoplasticneurological conditions: headache (4)*, trigeminal neuralgia, memoryproblem, gait difficulty, dementia, Parkinson disease, myelitis (2),normal pressure hydrocephalus, encephalitis, neuropathy, benigncerebellal lesion, Hodgkin disease with no CNS cancer. GBM 19Glioblastoma multiforme (glioma grade IV) Breast to 16 Breast cancermetastasis to brain Brain Breast LM 26 Breast cancer leptomeningialmetastasis Lung to 28 Lung cancer metastasis to brain Brain Lung LM 4Lung cancer leptomeningial metastasis N = number of patients per group.*The number of patients with a particular diagnosis, if more than one,is indicated in parenthesis.

TABLE 1B Neurological diagnosis and individual characteristics ofpatients included in CSF microRNA analysis Year of Clinical/PathologyTumor CSF sample Time/way of sample ## based diagnosis grade cytologyAge Gender collection collection Control (Non-neoplastic neurologicalconditions) 1 Non-specific pain No Negative 50 F 2005 No surgery/LPsyndrome tumor 2 Headache No Negative 33 F 2006 No surgery/LP tumor 3Memory No Negative 77 F 2006 No surgery/LP problems, gait tumordifficulty 4 Trigeminal No Negative 67 F 2005 No surgery/LP neuralgiatumor 5 Normal pressure No Negative 80 M 2006 No surgery/LPhydrocephalus tumor 6 Benign cerebellar No Negative 60 M 2006 Year aftersurgery/LP lesion tumor 7 Hodgkin's No Negative 33 F 2007 No surgery/LPdisease, no CNS tumor cancer 8 Neuropathy No Negative 28 F 2007 Nosurgery/LP tumor 9 Encephalitis in No Negative 63 M 2007 No surgery/LPpatient with tumor leukemia 10 Dementia No Negative 44 F 2007 Nosurgery/LP progressive tumor 11 Headache No Negative 25 M 2005 Nosurgery/LP tumor 12 Headache No Negative 40 F 2007 No surgery/LP tumor13 Parkinson Disease No Negative 71 M 2008 No surgery/LP tumor 14Transverse No Negative 43 F 2008 No surgery/LP myelitis tumor 15Transverse No Negative 31 F 2008 No surgery/LP myelitis tumor GBM:Glioblastoma multiforme 1 GBM IV Negative 55 F 2007 After surgery/LP/before chemoradiation 2 GBM IV Positive 27 F 2007 After surgery/Ommaya/after chemoradiation 3 GBM IV Positive 25 F 2008 After surgery/LP/afterchemoradiation 4 GBM IV Negative 28 M 2007 After surgery/LP/afterchemoradiation 5 GBM IV Positive 59 M 2007 After surgery/LP/afterchemoradiation 6 GBM IV Negative 32 M 2007 After surgery/LP/afterchemoradiation 7 GBM IV Negative 61 F 2008 After surgery/LP/afterchemoradiation 8 GBM IV Negative 63 M 2009 After surgery/LP/afterchemoradiation 9 GBM IV NA NA NA 2008 During surgery/ Ommaya/ beforechemoradiation 10 GBM IV NA NA NA 2008 During surgery/ Ommaya beforechemoradiation 11 GBM IV NA NA NA 2008 During surgery/ Ommaya beforechemoradiation 12 GBM IV NA NA NA 2008 During surgery/ Ommaya beforechemoradiation 13 GBM IV NA NA NA 2008 During surgery/ Ommaya beforechemoradiation 14 GBM IV NA NA NA 2008 During surgery/ Ommaya beforechemoradiation 15 GBM IV NA NA NA 2008 During surgery/ Ommaya beforechemoradiation 16 GBM IV NA NA NA 2008 During surgery/ Ommaya beforechemoradiation 17 GBM IV NA NA NA 2008 During surgery/ Ommaya beforechemoradiation 18 GBM IV Negative 61 F 2005 After surgery/LP/afterchemoradiation 19 GBM IV NA 43 F 2010 After surgery/LP/ beforechemoradiation Breast to Brain: breast cancer brain metastasis 1 Breastcarcinoma IV Positive 55 F 2008 No surgery/LP/after brain metastasisradiation and during chemotherapy 2 Breast carcinoma IV Positive 63 F2008 After brain metastasis surgery/Ommaya/ after radiation and duringchemotherapy 3 Breast carcinoma IV Positive 54 F 2008 Nosurgery/LP/after brain metastasis radiation and during chemotherapy 4Breast carcinoma IV Positive 60 F 2008 After brain metastasissurgery/Ommaya/ after radiation and during chemotherapy 5 Breastcarcinoma IV Positive 55 F 2008 After brain metastasis surgery/Ommaya/after radiation and during chemotherapy 6 Breast carcinoma IV Positive62 F 2008 After brain metastasis surgery/Ommaya/ after radiation andduring chemotherapy 7 Breast carcinoma IV Positive 54 F 2008 After brainmetastasis surgery/Ommaya/ after radiation and during chemotherapy 8Breast carcinoma IV Positive 60 F 2008 After surgery/LP/after brainmetastasis radiation and during chemotherapy 9 Breast carcinoma IVPositive 54 F 2008 After brain metastasis surgery/Ommaya/ afterradiation and during chemotherapy 10 Breast carcinoma IV Positive 52 F2008 No surgery/after brain metastasis radiation and during chemotherapy11 Breast carcinoma IV Positive 65 F 2008 After brain metastasissurgery/Ommaya/ after radiation and during chemotherapy 12 Breastcarcinoma IV Positive 48 F 2008 After brain metastasis surgery/Ommaya/after radiation and during chemotherapy 13 Breast carcinoma IV Positive46 F 2008 After surgery/LP/after brain metastasis radiation and duringchemotherapy 14 Breast carcinoma IV Atypical 50 F 2008 Aftersurgery/LP/after brain metastasis radiation and during chemotherapy 15Breast carcinoma IV Positive 55 F 2008 After surgery/LP/after brainmetastasis radiation and during chemotherapy 16 Breast carcinoma IVPositive 57 F 2008 After surgery/LP/after brain metastasis radiation andduring chemotherapy Breast LM: breast cancer leptomeningial metastasis 1Breast carcinoma IV Negative 42 F 2006 No surgery/LP/afterleptomeningial radiation metastasis 2 Breast carcinoma IV Positive 60 F2007 After leptomeningial surgery/Ommaya/ metastasis after radiation andduring chemotherapy 3 Breast carcinoma IV Positive 59 F 2007 Afterleptomeningial surgery/Ommaya/ metastasis after radiation and duringchemotherapy 4 Breast carcinoma IV Positive 61 F 2007 Afterleptomeningial surgery/Ommaya/ metastasis after radiation and duringchemotherapy 5 Breast carcinoma IV Positive 64 F 2007 Afterleptomeningial surgery/Ommaya/ metastasis after radiation and duringchemotherapy 6 Breast carcinoma IV Positive 53 F 2007 Afterleptomeningial surgery/Ommaya/ metastasis after radiation and duringchemotherapy 7 Breast carcinoma IV Positive 66 F 2007 Nosurgery/LP/after leptomeningial radiation metastasis 8 Breast carcinomaIV Positive 54 F 2007 After leptomeningial surgery/Ommaya/ metastasisafter radiation and during chemotherapy 9 Breast carcinoma IV Positive60 F 2007 After leptomeningial surgery/Ommaya/ metastasis afterradiation and during chemotherapy 10 Breast carcinoma IV Positive 63 F2007 After leptomeningial surgery/Ommaya/ metastasis after radiation andduring chemotherapy 11 Breast carcinoma IV Positive 66 F 2007 Afterleptomeningial surgery/Ommaya/ metastasis after radiation and duringchemotherapy 12 Breast carcinoma IV Positive 60 F 2007 Afterleptomeningial surgery/Ommaya/ metastasis after radiation and duringchemotherapy 13 Breast carcinoma IV Positive 55 F 2007 Afterleptomeningial surgery/Ommaya/ metastasis after radiation and duringchemotherapy 14 Breast carcinoma IV Positive 56 F 2007 Afterleptomeningial surgery/Ommaya/ metastasis after radiation and duringchemotherapy 15 Breast carcinoma IV Positive 44 F 2007 Afterleptomeningial surgery/Ommaya/after metastasis radiation and duringchemotherapy 16 Breast carcinoma IV Positive 58 F 2007 Afterleptomeningial surgery/Ommaya/ metastasis after radiation and duringchemotherapy 17 Breast carcinoma IV Positive 54 F 2007 Nosurgery/LP/after leptomeningial radiation and during metastasischemotherapy 18 Breast carcinoma IV Negative 45 F 2007 Nosurgery/LP/after leptomeningial radiation and during metastasischemotherapy 19 Breast carcinoma IV Negative 60 F 2008 Nosurgery/LP/after leptomeningial radiation and during metastasischemotherapy 20 Breast carcinoma IV Positive 51 F 2008 Afterleptomeningial surgery/Ommaya/ metastasis after radiation and duringchemotherapy 21 Breast carcinoma IV Positive 29 F 2008 Nosurgery/LP/after leptomeningial radiation and during metastasischemotherapy 22 Breast carcinoma IV Positive 69 F 2008 Nosurgery/LP/after leptomeningial radiation and during metastasischemotherapy 23 Breast carcinoma IV Positive 61 F 2008 NA leptomeningialmetastasis 24 Breast carcinoma IV Positive 64 F 2008 No surgery/LP/afterleptomeningial radiation and during metastasis chemotherapy 25 Breastcarcinoma IV Positive 63 F 2008 No surgery/LP leptomeningial metastasis26 Breast carcinoma IV Positive 59 F 2008 After leptomeningialsurgery/Ommaya/ metastasis after radiation and during chemotherapy Lungto Brain: lung cancer brain metastasis 1 Lung cancer brain IV Positive56 F 2007 No surgery/LP/after metastasis radiation and duringchemotherapy 2 Lung cancer brain IV Positive 59 F 2007 Nosurgery/LP/after metastasis radiation and during chemotherapy 3 Lungcancer brain IV Positive 56 F 2007 No surgery/LP/after metastasisradiation and during chemotherapy 4 Lung cancer brain IV Positive 68 F2007 No surgery/LP metastasis 5 Lung cancer brain IV Positive 69 M 2007No surgery/LP/after metastasis radiation 6 Lung cancer brain IV Positive71 M 2007 No surgery/LP/after metastasis radiation and duringchemotherapy 7 Lung cancer brain IV Positive 66 F 2007 Nosurgery/LP/after metastasis radiation and during chemotherapy 8 Lungcancer brain IV Positive 63 F 2007 No surgery/LP/after metastasisradiation and during chemotherapy 9 Lung cancer brain IV Positive 60 F2007 No surgery/LP/after metastasis radiation and during chemotherapy 10Lung cancer brain IV Positive 59 F 2007 No surgery/LP metastasis 11 Lungcancer brain IV Positive 55 M 2008 No surgery/LP metastasis 12 NSCLCbrain IV Negative 66 F 2008 No surgery/LP metastasis 13 Lung cancerbrain IV Positive 62 F 2007 No surgery/LP/after metastasis radiation andduring chemotherapy 14 Lung cancer brain IV Positive 64 F 2006 Nosurgery/LP metastasis 15 Lung cancer brain IV Positive 64 F 2006 Nosurgery/LP metastasis 16 Lung cancer brain IV Negative 46 F 2007 Nosurgery/LP metastasis 17 Lung cancer brain IV Positive 64 F 2007 Nosurgery/LP metastasis 18 NSLC brain IV Negative 50 M 2007 No surgery/LPmetastasis 19 NSCLC brain IV Positive 56 M 2007 No surgery/LP/aftermetastasis radiation and during chemotherapy 20 NSCLC brain IV Positive49 F 2007 No surgery/LP/after metastasis radiation and duringchemotherapy 21 Lung cancer brain IV Positive 42 M 2007 Nosurgery/LP/after metastasis radiation and during chemotherapy 22 Lungcancer brain IV Positive 56 F 2007 No surgery/LP/after metastasisradiation and during chemotherapy 23 Lung cancer brain IV Positive 58 F2008 No surgery/LP/after metastasis radiation and during chemotherapy 24NSCLC brain IV Positive 48 M 2008 No surgery/LP metastasis 25 MSCLCbrain IV Negative 54 F 2008 No surgery/LP metastasis 26 NSCLC brain IVNegative 61 F 2008 No surgery/LP metastasis 27 NSCLC brain IV NA 51 F2010 After surgery/ metastasis Ommaya after radiation and duringchemotherapy 28 NSCLC brain IV NA 66 F 2010 No surgery/LP aftermetastasis radiation and during chemotherapy Lung LM: lung cancerleptomeningial metastasis 1 Lung cancer IV Positive 67 F 2006 Nosurgery/LP leptomeningial metastasis 2 SCLC IV Negative 52 M 2007 Nosurgery/LP leptomeningial metastasis 3 Lung cancer IV Negative 56 F 2008No surgery/LP leptomeningial metastasis 4 NSCLC IV NA 63 M 2010 Nosurgery/LP/after leptomeningial radiation and metastasis chemotherapy NA= not available, NSCLC—non-small cell lung carcinoma, SCLC—small celllung carcinoma/

Statistical Analysis and Support Vector Machine (SVM)-Based DataClassification.

The differences in CSF miRNAs levels between groups of samples weredetermined using Graph Pad Prism software by Wilcoxon Signed Rank test,and two-tailed P-values were calculated.

SVM was implemented within a machine learning software package weka(Witten, “Data Mining: Practical machine learning tools and techniques,3rd Edition”. Morgan Kaufmann, San Francisco (2011)), available on theinternet at cs.waikato.ac.nz/ml/weka. In such an approach, a sample'smiRNA levels were treated as independent variables and the type ofcancer, if any, as a variable to be predicted. The SVM was trained andtested on such a dataset, using standard N-fold cross-validationprocess. In this process the SVM was trained on all samples, except forone, and tested on that holdout sample. The procedure was repeated asmany times as there were samples in the dataset, hence each sample onceand only once forms the holdout set. The following choices ofnon-default parameters working best: Classifier: SMO, kernel RBF,Complexity parameter=one for all tasks, except breast vs. lungmetastasis, in which case it was 100. Ct data were used for theclassification as is, with no standardization or normalization, except“1000” was used on the place of Ct values in the cases of undetectablemiRNA.

The Cancer Genome Atlas (TCGA) miRNA expression microarray data for GBMpatients were downloaded from tcga-data.nci.nih.gov/tcga/homepage.htm;see Hudson et al., Nature 464:993-998 (2010). The fold difference inspecific signals between GBM (n=261) and normal brain (n=10) tissue werecalculated for each miRNA as described³.

Example 1. miR-10b is Present and miR-21 is Elevated in CSF ofGlioblastoma and Brain Metastasis Patients

To identify miRNA biomarkers for GBM, a candidate approach was usedbased on previous miRNA profiling data^(3, 14, 15). An additionalanalysis of miRNA expression in 261 GBM patients utilized The CancerGenome Atlas (TCGA) dataset (Hudson et al., Nature 464:993-998 (2010))and revealed a panel of miRNAs deregulated in GBM relative to normalbrain tissues (FIG. 1A). Among them, miR-10b and miR-21 were the moststrongly up-regulated (FIG. 1A). miR-10b is a unique molecule, as it isthe only known miRNA undetectable in normal brain while highly expressedin GBM^(16, 17). It was therefore chosen as the top priority candidate.Expression of miR-10b is also associated with metastatic phenotypes ofseveral solid cancers, including breast and lung cancers^(18, 19).

miR-10b levels were examined in the CSF samples of the study cohortpatients, and miR-10b-specific qRT-PCR product was detected in CSF of 17out of 19 GBM patients (89% cases, FIG. 1B). This is consistent withprevious finding of miR-10b expression in ˜90% of GBM tumors¹⁵. miR-10bwas also detected in CSF of 81% of patients with brain andleptomeningeal metastasis of both breast and lung cancer (FIG. 1B). Noneof the patients with various non-neoplastic neurological conditionsshowed detectable levels of miR-10b at 40 cycles of the qRT-PCRreaction. Raw qRT-PCR Ct values representing specific CSF levels ofmiR-10b and other miRNAs are shown in Table 2B. Therefore, miR-10b inCSF is a highly indicative marker of high-grade primary and metastaticbrain cancers.

Next CSF levels were assessed for another candidate miRNA, miR-21, whichis the most common miRNA elevated in GBM and other cancers²⁰ and alsomost strongly up-regulated in GBM as compared to normal brain (FIG. 1A).miR-21 levels are significantly increased in CSF of most GBM andmetastatic patients relatively to its levels in the control CSF samples(FIG. 1C), suggesting that it may represent an additional CSF biomarkerfor both GBM and metastatic brain cancer.

The levels of three additional candidate miRNAs upregulated in GBMrelative to normal brain, miR-15b, miR-17-5p and miR-93 (FIG. 1A), havebeen determined in a randomly selected set of several CSF samples. Thelevels of all three miRNAs were higher in CSF of GBM and metastaticbrain cancer patients relative to the non-neoplastic controls (FIGS. 6A,C, E); however, these differences have not reached the significance andwere abolished by data normalization to miR-125b (FIGS. 6B, D, F).

TABLE 2A Accuracies of classification of brain tumors by SVM analysis.Instances classified in the test sets Comparison Correctly IncorrectlyGBM versus non-neoplastic 31 (91.2%) 3 (8.8%) controls Metastasis versusnon-neoplastic 88 (98.9%) 1 (1.1%) controls GBM and metastasis versus105 (97.2%)  3 (2.8%) non-neoplastic controls GBM versus metastasis 89(95.7%) 4 (4.3%) GBM versus non-GBM 102 (94.5%)  6 (5.5%) (all others)Metastasis versus 100 (92.6%)  8 (7.4%) non-metastasis (all others)Breast versus lung metastasis 51 (68.9%) 23 (31.1%)

TABLE 2B miRNA Type # 125b 10b 21 141 200a 200b 200c Non-neoplastic 134.2697 UD 33.3324 UD UD UD UD Non-neoplastic 1 33.9405 UD 33.0829 UD UDUD UD Non-neoplastic 2 33.0152 UD 33.5002 UD UD UD UD Non-neoplastic 232.799 UD 32.9746 UD UD UD UD Non-neoplastic 3 32.9036 UD 33.707 UD UDUD UD Non-neoplastic 3 33.5036 UD 33.5222 UD UD UD UD Non-neoplastic 432.1067 UD 32.5033 UD UD UD UD Non-neoplastic 4 32.2493 UD 32.8214 UD UDUD UD Non-neoplastic 5 33.8516 UD 33.258 UD UD UD UD Non-neoplastic 535.8576 UD 32.7309 UD UD UD UD Non-neoplastic 6 32.4644 UD 28.6672 UD UDUD UD Non-neoplastic 6 32.4621 UD 28.7054 UD UD UD UD Non-neoplastic 731.6864 UD 35.2616 UD 37.4531 UD UD Non-neoplastic 7 32.1712 UD 35.0806UD 37.2431 UD UD Non-neoplastic 8 32.0006 UD 32.1841 UD UD UD UDNon-neoplastic 8 31.7911 UD 31.7029 UD UD UD UD Non-neoplastic 9 34.5177UD 30.3603 UD UD UD UD Non-neoplastic 9 35.5515 UD 30.6514 UD UD UD UDNon-neoplastic 10 32.5169 UD 32.9137 UD UD UD UD Non-neoplastic 1032.781 UD 32.3816 UD UD UD UD Non-neoplastic 11 30.661 UD 30.635 UD UDUD UD Non-neoplastic 11 30.706 UD 30.528 UD UD UD UD Non-neoplastic 1230.396 UD 30.993 UD UD UD UD Non-neoplastic 12 30.159 UD 31.398 UD UD UDUD Non-neoplastic 13 29.798 UD 38.9142 UD UD UD UD Non-neoplastic 1329.469 UD 38.9142 UD UD UD UD Non-neoplastic 14 37.111 UD 36.431 UD UDUD UD Non-neoplastic 14 36.750 UD 35.824 UD UD UD UD Non-neoplastic 1532.311 UD 33.307 UD UD UD UD Non-neoplastic 15 31.782 UD 33.483 UD UD UDUD GBM 1 28.493 35.4474 24.8591 UD UD UD UD GBM 1 28.3347 36.166925.0358 UD UD UD UD GBM 2 30.27 UD 28.5448 UD UD UD UD GBM 2 29.8595 UD28.7406 UD UD UD UD GBM 3 25.5607 33.3961 22.0836 36.807 33.5488 36.665836.6814 GBM 3 24.3582 33.0576 22.1982 35.7105 33.2086 37.0597 37.1643GBM 4 24.9425 37.8446 23.4126 UD 35.597 UD 34.1835 GBM 4 24.8871 37.068122.9477 UD 35.0309 UD 34.1049 GBM 5 34.2504 UD 33.3238 UD UD UD UD GBM 534.4141 UD 33.2358 UD UD UD UD GBM 6 25.9917 36.2066 21.9135 UD 35.2526UD UD GBM 6 25.7625 36.2066 21.6147 UD 34.1246 UD UD GBM 7 29.295933.4857 29.3222 UD 37.1513 UD UD GBM 7 29.1532 33.1848 28.6781 UD36.9511 UD UD GBM 8 29.7628 30.8808 33.2773 UD UD UD UD GBM 8 29.669630.7112 32.7008 UD UD UD UD GBM 9 29.5463 36.926 22.4494 28.5888 UD31.3221 UD GBM 9 29.8912 38.0723 22.4455 29.173 UD 31.7444 UD GBM 1018.8301 28.2565 21.3035 34.1768 30.673 35.202 30.9622 GBM 10 19.178128.3153 20.1106 35.3052 31.3501 34.5208 32.0136 GBM 11 19.0653 25.399219.9446 35.7793 30.3237 34.3587 35.3505 GBM 11 19.0975 25.3985 20.591735.4663 29.8643 34.234 36.6375 GBM 12 21.4785 29.5007 22.5529 34.393832.3403 36.3228 33.6589 GBM 12 21.4785 30.5404 22.0745 35.6437 32.856535.9838 33.3638 GBM 13 20.6069 28.0427 22.8669 38.4408 29.7108 34.463831.5322 GBM 13 21.1061 27.6744 22.4195 36.4015 31.1373 33.8695 32.1085GBM 14 20.5726 29.0133 19.8893 35.0699 31.0412 35.4186 32.4751 GBM 1420.4409 29.2476 20.1753 36.0567 31.5226 35.4393 33.3155 GBM 15 28.042934.4698 31.1034 UD UD UD UD GBM 15 28.3493 34.9682 31.2799 UD UD UD UDGBM 16 18.9454 29.2594 20.2101 33.9212 UD 34.7543 30.0307 GBM 16 19.094929.0995 19.8017 34.5306 UD 34.0056 31.1451 GBM 17 19.0563 25.713 19.684135.3343 28.5198 31.2043 31.0678 GBM 17 19.3106 26.0705 19.6881 35.019429.3597 31.4789 31.798 GBM 18 31.138 34.459 26.774 UD UD UD UD GBM 1831.555 35.215 26.695 UD UD UD UD GBM 19 28.157 33.496 27.861 UD UD UD UDGBM 19 27.883 34.539 27.602 UD UD UD UD Breast to Brain 1 27.817432.0139 21.1639 29.5078 26.0618 31.4264 27.1292 Breast to Brain 127.2568 31.706 20.675 29.3259 26.2505 30.9209 27.7123 Breast to Brain 232.6303 UD 28.0095 37.1365 31.0578 32.6672 31.5072 Breast to Brain 232.5818 UD 27.7492 37.6775 31.0501 32.4441 31.8525 Breast to Brain 325.7808 31.3092 20.1414 29.1359 27.1009 30.5338 28.0328 Breast to Brain3 25.977 31.3399 20.1774 29.2168 26.8024 30.2247 28.4686 Breast to Brain4 31.1532 38.8239 23.4787 32.0578 26.4437 29.4728 30.6951 Breast toBrain 4 31.3755 UD 23.5862 32.8802 26.9978 29.1922 31.5229 Breast toBrain 5 29.6268 36.8038 25.6345 29.8542 24.483 27.1907 28.9925 Breast toBrain 5 30.2187 36.262 25.0105 32.3864 24.483 27.2909 29.4038 Breast toBrain 6 30.3481 UD 25.5752 30.7873 24.67 28.5216 26.9064 Breast to Brain6 30.709 UD 27.1514 31.7873 24.7185 28.2027 26.8947 Breast to Brain 735.4251 36.5204 28.0536 32.7134 27.8074 30.2571 32.2786 Breast to Brain7 35.9251 36.5204 28.2612 32.3935 28.0258 29.9113 33.1268 Breast toBrain 8 30.5423 36.5667 27.8147 32.3054 29.5245 32.3943 29.0791 Breastto Brain 8 30.1858 36.8752 27.8631 32.1674 29.9147 32.5332 28.0088Breast to Brain 9 32.1644 UD 25.9139 31.7038 28.1264 30.3435 30.0191Breast to Brain 9 33.1737 UD 25.8558 32.1792 28.1041 30.1035 30.2432Breast to Brain 10 28.3774 37.1231 25.108 28.4444 27.2268 31.083426.2144 Breast to Brain 10 28.8228 36.1869 25.0972 28.835 26.549931.1109 25.9687 Breast to Brain 11 33.2952 UD 30.864 UD 33.4073 38.179633.7632 Breast to Brain 11 32.6806 UD 30.8002 UD 35.7065 37.0988 33.3951Breast to Brain 12 30.044 32.846 25.180 30.020 30.641 32.699 29.391Breast to Brain 12 29.709 34.234 25.414 30.461 30.452 32.992 30.033Breast to Brain 13 30.368 36.826 27.307 33.816 33.117 35.072 31.908Breast to Brain 13 30.417 36.920 27.261 33.340 32.604 35.081 32.021Breast to Brain 14 21.508 25.708 23.920 35.289 35.603 35.800 34.705Breast to Brain 14 21.414 25.617 23.742 36.763 35.476 38.213 34.781Breast to Brain 15 29.378 36.876 26.886 30.667 30.539 32.789 29.405Breast to Brain 15 29.457 36.376 26.601 30.678 30.333 32.183 29.738Breast to Brain 16 30.966 36.324 30.592 34.492 34.035 36.778 32.881Breast to Brain 16 30.699 37.014 30.740 34.933 33.617 36.980 32.690Breast LM 1 30.631 35.604 28.651 35.954 35.557 UD 35.152 Breast LM 130.519 35.568 28.452 37.282 35.763 38.580 UD Breast LM 2 26.997 34.31820.781 29.000 26.659 28.954 27.883 Breast LM 2 26.886 34.178 20.39529.111 26.412 28.871 28.265 Breast LM 3 24.423 31.054 19.165 27.76725.225 27.433 26.237 Breast LM 3 24.284 31.130 18.992 27.967 25.00827.407 26.622 Breast LM 4 28.283 35.548 22.324 30.800 26.526 30.47029.647 Breast LM 4 28.123 34.502 22.095 30.900 26.425 30.638 29.759Breast LM 5 24.748 31.465 19.238 29.508 26.466 28.156 27.618 Breast LM 524.735 31.253 19.162 29.591 26.347 28.039 27.623 Breast LM 6 25.16431.746 19.547 29.870 27.440 28.653 28.036 Breast LM 6 25.097 31.74219.467 30.269 27.271 28.579 28.192 Breast LM 7 31.297 34.899 28.89538.345 36.188 UD 28.182 Breast LM 7 31.275 34.054 28.710 38.815 36.763UD 28.202 Breast LM 8 25.550 31.414 20.539 30.363 28.001 30.203 29.081Breast LM 8 25.382 31.941 20.389 31.110 28.097 29.728 29.224 Breast LM 925.436 32.248 19.751 29.839 27.778 29.802 28.736 Breast LM 9 25.38132.310 19.668 30.266 27.705 29.566 29.577 Breast LM 10 26.174 32.97020.036 32.305 28.691 30.722 29.632 Breast LM 10 26.062 32.313 19.91632.080 28.712 31.071 29.973 Breast LM 11 29.221 35.174 24.557 36.69133.055 33.915 32.966 Breast LM 11 29.204 34.316 24.509 36.177 32.81533.137 33.631 Breast LM 12 30.453 UD 27.958 33.654 30.871 33.833 31.953Breast LM 12 30.371 UD 28.002 33.772 30.846 33.242 32.321 Breast LM 1327.006 33.424 22.239 33.263 29.571 30.881 30.444 Breast LM 13 27.00633.535 22.293 33.286 29.470 30.810 30.672 Breast LM 14 25.784 33.43620.025 27.453 24.736 26.462 25.903 Breast LM 14 25.723 33.897 19.95327.674 24.601 26.389 26.229 Breast LM 15 28.633 34.998 26.284 32.96130.162 31.955 30.838 Breast LM 15 28.428 35.181 26.165 33.110 30.14831.753 31.015 Breast LM 16 28.807 35.442 26.537 32.348 30.373 32.30131.592 Breast LM 16 28.680 34.988 26.355 33.175 30.416 32.011 31.681Breast LM 17 29.268 24.630 21.239 29.995 28.911 29.920 27.692 Breast LM17 29.097 24.605 20.887 30.363 28.886 29.762 28.305 Breast LM 18 29.70231.968 26.406 31.073 30.501 32.820 29.712 Breast LM 18 29.969 31.51426.260 31.508 30.430 32.741 29.802 Breast LM 19 26.527 31.477 22.03528.358 30.716 30.165 26.926 Breast LM 19 26.526 31.654 21.967 28.39230.713 30.015 27.044 Breast LM 20 26.373 35.276 19.590 26.371 27.90125.011 24.178 Breast LM 20 26.270 34.665 19.544 26.438 27.631 25.08924.138 Breast LM 21 28.123 34.414 23.245 29.885 23.275 31.398 28.881Breast LM 21 28.134 34.245 23.257 29.831 23.046 31.542 28.934 Breast LM22 32.904 UD 29.293 34.773 34.715 36.438 33.616 Breast LM 22 33.028 UD29.127 34.571 34.321 37.548 33.449 Breast LM 23 27.233 35.308 21.98628.639 29.883 31.056 27.869 Breast LM 23 27.156 36.094 22.032 28.65429.878 31.049 28.177 Breast LM 24 28.149 33.316 25.137 27.720 27.84230.901 26.319 Breast LM 24 27.947 32.855 24.882 27.926 27.995 30.79326.763 Breast LM 25 27.659 34.227 19.330 26.775 23.657 24.032 23.402Breast LM 25 27.362 34.603 19.135 27.104 23.416 23.953 24.071 Breast LM26 31.169 UD 25.420 28.289 25.468 30.137 26.360 Breast LM 26 30.721 UD25.250 28.572 25.305 30.119 26.642 Lung to Brain 1 27.3027 31.549622.65115 25.3368 25.2186 28.9333 24.1757 Lung to Brain 1 27.2988 31.105823.05115 25.3807 25.1565 28.3453 23.8634 Lung to Brain 2 29.8443 34.849725.1519 32.1757 31.3363 34.9516 30.1594 Lung to Brain 2 29.7741 34.725324.5772 31.9565 31.6946 35.3302 29.1884 Lung to Brain 3 33.0843 UD29.3511 34.3175 34.4514 37.0247 33.1313 Lung to Brain 3 33.5869 UD29.4506 34.7511 34.8228 36.7855 32.3656 Lung to Brain 4 32.6941 UD28.2911 33.9836 31.5455 33.2976 30.4481 Lung to Brain 4 32.6056 UD27.1608 32.7802 31.2428 33.0444 30.4042 Lung to Brain 5 30.2049 34.896824.7768 30.4436 28.4256 30.2405 27.5537 Lung to Brain 5 29.5105 34.772524.0629 30.5538 28.1955 29.9892 27.1272 Lung to Brain 6 32.5851 36.625529.7253 35.4127 33.7658 35.5324 30.491 Lung to Brain 6 32.7851 37.444329.7184 35.1166 33.1176 35.0508 31.0042 Lung to Brain 7 29.261 33.499124.232 28.9268 28.5605 30.46 27.6959 Lung to Brain 7 28.4163 33.066323.8848 28.9189 28.312 30.706 27.9061 Lung to Brain 8 30.4814 34.68722.3076 28.6553 27.6452 30.3316 29.4116 Lung to Brain 8 30.776 35.204721.9802 29.0701 27.6333 29.272 28.661 Lung to Brain 9 30.2956 34.34926.8941 30.8863 29.4441 31.3527 31.2236 Lung to Brain 9 29.9115 33.538427.4941 31.091 29.5607 31.5945 29.1472 Lung to Brain 10 29.1638 35.025522.6924 29.9554 32.817 31.0666 27.3901 Lung to Brain 10 29.4353 34.496623.1541 30.097 32.9107 31.0331 27.2599 Lung to Brain 11 27.4463 33.465221.1578 26.9988 25.7732 28.9661 25.0689 Lung to Brain 11 27.3261 34.137120.9667 26.3149 25.4019 28.0832 24.8732 Lung to Brain 12 32.8667 UD30.8165 UD UD UD 38.2814 Lung to Brain 12 32.2667 UD 30.3494 UD UD UD37.08 Lung to Brain 13 34.1699 UD 24.4215 30.4942 29.2874 31.581331.9309 Lung to Brain 13 34.2134 UD 24.2206 30.0906 29.0842 31.581332.2984 Lung to Brain 14 29.293 34.571 24.394 30.789 29.544 33.05728.864 Lung to Brain 14 29.009 35.563 24.532 30.838 29.377 32.956 28.902Lung to Brain 15 28.914 34.550 22.560 29.644 28.600 30.866 27.167 Lungto Brain 15 28.707 34.495 22.627 29.678 28.693 30.347 27.103 Lung toBrain 16 26.601 31.991 22.155 27.351 26.558 28.982 26.586 Lung to Brain16 26.458 32.220 22.243 27.760 26.265 28.980 27.004 Lung to Brain 1730.365 35.322 22.837 28.904 28.364 30.994 27.650 Lung to Brain 17 30.36835.505 22.640 28.751 27.744 31.052 27.517 Lung to Brain 18 30.310 35.76229.548 34.882 35.961 39.607 33.730 Lung to Brain 18 30.162 37.352 29.50135.203 35.808 38.411 34.555 Lung to Brain 19 29.630 32.016 24.964 27.43128.617 30.526 27.507 Lung to Brain 19 29.594 31.720 24.962 27.681 28.63230.398 27.934 Lung to Brain 20 28.500 UD 23.147 26.762 28.607 29.80125.805 Lung to Brain 20 28.472 UD 23.183 26.857 28.429 29.778 25.829Lung to Brain 21 26.383 33.937 21.266 29.484 30.964 31.936 28.164 Lungto Brain 21 26.398 33.081 21.299 29.664 30.766 31.886 28.331 Lung toBrain 22 27.589 36.414 24.198 31.107 33.120 35.063 30.855 Lung to Brain22 27.681 36.387 24.163 31.499 32.544 34.379 30.925 Lung to Brain 2327.335 33.311 20.275 26.183 27.803 29.310 26.190 Lung to Brain 23 27.20332.897 20.198 26.497 27.698 29.155 26.421 Lung to Brain 24 31.188 33.76124.351 30.843 31.061 32.678 30.078 Lung to Brain 24 31.066 34.498 24.57631.006 30.770 32.639 29.865 Lung to Brain 25 25.438 33.677 22.276 27.03026.485 28.167 25.754 Lung to Brain 25 25.257 32.734 22.333 27.055 26.32028.058 25.845 Lung to Brain 26 27.957 35.622 26.272 30.664 29.900 32.14528.598 Lung to Brain 26 27.770 35.349 25.912 30.721 29.989 32.029 28.710Lung to Brain 27 27.791 35.924 23.314 30.597 29.887 31.737 29.783 Lungto Brain 27 27.719 36.972 22.870 31.188 29.900 32.049 30.955 Lung toBrain 28 27.600 34.338 22.529 26.370 28.088 31.174 26.558 Lung to Brain28 27.498 34.905 21.968 26.742 27.800 31.009 26.244 Lung LM 1 28.65230.282 22.137 25.738 24.665 27.190 25.600 Lung LM 1 28.606 30.400 21.84326.250 24.557 27.097 25.948 Lung LM 2 27.795 33.788 24.948 39.425 36.26137.184 37.034 Lung LM 2 27.934 32.653 24.846 38.606 36.606 37.702 36.898Lung LM 3 27.478 37.812 31.801 29.974 29.569 31.303 28.059 Lung LM 327.310 37.200 31.664 30.034 29.446 31.181 28.566 Lung LM 4 27.588 32.72619.656 24.357 24.419 27.413 24.179 Lung LM 4 27.627 32.723 19.472 24.37624.369 27.213 24.289 UD = Undetermined

Example 2. miR-200 Family in the CSF is Indicative of Brain Metastasis

miR-10b is expressed in most extracranial tissues^(21, 22) (FIGS. 7A-B),and abundant in blood serum²³. However, it is not expressed in brain andnot detectable in CSF of non-cancer patients. Therefore, miR-10b andother miRNAs seem unlikely to pass the blood-brain barrier undernon-neoplastic conditions, and miRNAs in CSF might therefore reflect aunique miRNA signature of brain. On the other hand, miR-10b is highlyexpressed in breast and lung tissues, and its presence in the CSF oflung and breast cancer patients with CNS metastasis indicates thatmetastatic cells bring their signature miRNAs to the CSF. Based on thesedata, other miRNA CSF biomarkers were sought that could enablediscrimination between GBM and metastatic brain tumors. Such miRNAsshould be highly expressed in a primary carcinoma or tissues of itsorigin (e.g. lung or breast) but not in brain or GBM.

According to miRNA profiling across different tissues, miRNAs of miR-200family are good candidates fulfilling this criteria. All members of thisfamily are highly expressed in lung and breast tissues and epithelialcancers, including lung and breast carcinomas, but are barely detectablein brain^(22, 24) and FIGS. 8A-B). On the other hand the miR-200 family,unlike miR-10b, is not expressed in GBM and other primary brain tumors,making it a putative biomarker for metastatic brain cancer (FIG. 2A).

To explore a potential of miRNA-200 for distinguishing between GBM andmetastatic brain cancer, the levels of four miR-200 family members,miR-200a, miR-200b, miR-200c and miR-141, were assessed in CSF ofcontrol, GBM and metastatic brain cancer patients. Remarkably, all fourmiRNAs were highly expressed in the majority of CSF samples collectedfrom the patients with brain and leptomeningial metastasis, but not inthe control or GBM cases (FIG. 2B-E). These data suggest miR-200 levelsmight be used for discriminating between primary brain cancer and brainmetastasis.

In attempt to discriminate between metastasis from breast vs. lungcancer, miR-195 levels were assessed in several randomly selected CSFsamples, since circulating miR-195 was proposed as a differentialbiomarker of breast vs. lung cancer²⁵. However, no significantdifferences were found in miR-195 levels in CSF of breast and lungcancer metastasis patients (FIG. 9). Another miRNA, miR-1 is expressedat higher levels in breast versus lung tissue according to miRNAexpression profiles²² but miR-1 was undetectable in CSF of both breastand lung cancer cohorts of patients. Breast and lung carcinomas expressstrikingly similar miRNA repertoire²¹. However, there were significantlyhigher amounts of miR-200a and miR-200b (two miRNAs encoded as a clusterat chromosome 1p36.33) in CSF of the patients with breast cancerrelative to lung cancer, while CSF levels of miR-141 and -200c(co-encoded at chromosome 12p13.31) were similar in breast and lungcancer cases (FIG. 2F). These data suggest that the ratios betweenmiRNAs of two different miR-200 genomic clusters in CSF may beinformative for discrimination between brain metastasis from breastversus lung cancer.

Example 3. Computational Classification of High-Grade Brain MalignanciesBased on CSF miRNA Profiling

The relationships discovered between the miRNA CSF levels and diagnosticoutcomes are illustrated by a simple diagnostic decision tree (FIG. 3A).The next experiments tested whether the samples can be classified intoclasses more accurately (non-neoplastic control vs. GBM vs. metastasis)using a “machine-learning technique” based on Support Vector Machine(SVM) concept. This technique was previously applied to a wide range ofbiological problems, including mRNA and miRNA expression data analysisin cancers²⁶⁻²⁸.

Various SVM algorithms were applied for classification of the samples.In one case (GBM vs. metastasis classification) a very simple linearclassifier provides discrimination with about 95% accuracy. The levelsof two miRNAs, miR-200a and miR-125b were used in this case asindependent variables, and a linear function of these two Ct levelsemployed as a classifier with the coefficients calculated in the processof the classifier training.

Another case that allows for a similar interpretation is theclassification of GBM and brain metastasis versus non-neoplasticcontrols. In that case a linear classifier was constructed that uses Ctlevels of three miRNAs: miR-10b, miR-200a and miR-125b as features.Accordingly, a two-dimensional plane in the space spawned by the levelsof these three miRNAs separated the space into two domains.

Linear algorithms provided satisfactory classification for GBM vMetastasis (using the formula0.3364*miR-125b+0.0808*miR-10b+0.4578*miR-21+−0.0871*miR-141+0.001*miR-200a+0.0213*miR-200b+−0.3419*miR-200c−7.2516);GBM and metastasis versus non-neoplastic(0.0003*miR-125b+−0.0021*miR-10b+−0.0002*miR-21+0*miR-141+0*miR-200a+0*miR-200b+−0.0021*miR-200c+3.1536);GBM versus non-neoplastic(0.0002*miR-125b+0.0021*miR-10b+−0.0001*miR-21+0*miR-141+0*miR-200a+0*miR-200b+0*miR-200c−1.0849);

Metastases versus non-neoplastic(0*miR-125b+0*miR-10b+0*miR-21+0*miR-141+0*miR-200a+0*miR-200b+0.0021*miR-200c−1.0744);GBM versus non-GBM (all others)(0.2468*miR-125b+0.1816*miR-10b+0.107*miR-21+0.0007*miR-141+0.0003*miR-200a+−0.0032*miR-200b+−0.1817*miR-200c−7.7752);Metastasis versus non-metastasis (all others)(0.3348*miR-125b+0.0838*miR-10b+0.4619*miR-21+−0.0902*miR-141+0.001*miR-200a+0.0284*miR-200b+−0.3482*miR-200c−7.3231);Breast versus lung(0.1592*miR-125b+−0.0003*miR-10b+0.0381*miR-21+−0.5325*miR-141+0.5346*miR-200a+−0.0014*miR-200b+−0.1282*miR-200c−1.0529).In each case, a negative result puts the sample into the first class,and a positive result puts the sample into the second class.

Similarly, various SVM classifiers were tested and the RBF kernelprovided good separation between all classes of samples. The bestclassification accuracy was achieved using the levels of seven miRNAs:miR-10b, miR-21, miR-125b, miR-141, miR-200a, miR-200b, and miR-200c asindependent variables.

This analysis revealed that different types of cancer are distinguishedfrom each other as well as from non-neoplastic control with the averagecross-validation accuracy of about 90% (Table 2A). That means that theSVM incorrectly predicted the class of about one of ten previouslyunseen samples. This analysis suggests a possibility of computationaldifferential diagnostics of brain cancers using miRNA profiling.

Example 4. The Origin of miRNA in CSF

miRNAs detected in the CSF of brain cancer patients may originate frombrain tumor cells, from surrounding brain tissues or from extracranialtissues due to the blood-brain barrier disruption associated with tumorgrowth. To discriminate between these possibilities miR-10b and miR-21expression levels were determined in tumor biopsies obtained duringbrain surgery and corresponding CSF samples from the same patients. Apositive correlation was observed between miR-10b expression level inthe brain tumor and corresponding CSF specimens, and no such correlationwas observed for miR-21 (FIG. 3B). Of note, miR-10b is expressed intumors but not in normal brain tissues, while miR-21 is elevated intumors but is also present in normal brain^(14, 16) Taking theseexpression patterns into account, the data suggest that miRNAcomposition of the CSF is established by tumor cells as well as by thecells of surrounding brain tissues.

Example 5. miRNAs in CSF of Brain Cancer Patients as Markers of DiseaseActivity

To examine whether CSF levels of miRNAs reflect a diseasestatus/activity, miRNA was studied in CSF of active GBM and metastaticbrain cancer versus tumor remission cases. The disease was considered inremission if, following treatment, there were no evidence of tumor massdetected by MRI and CSF cytological analysis was negative. NeithermiR-10b nor miR-200 family members were detected after 40 cycles ofqRT-PCR reaction in CSF samples in any of remission cases (Table 3,FIGS. 10A-F). MiR-21 levels were significantly lower in cancer remissioncases as compared to active GBM and metastatic brain cancer cases beforetreatment (FIG. 10B). These data suggest that miRNAs analyzed in thisstudy may reflect the activity of brain tumors.

To further test whether the CSF levels of specific miRNAs reflect thedisease status/activity and responsiveness to therapy, miRNA levels weredetermined in CSF of lung cancer and GBM patients longitudinally duringcourse of erlotinib treatment. miRNA analysis was accompanied by MRI,CSF cytology, and clinical monitoring of the disease status. A NSCLCpatient (patient A) developed parenchymal and leptomeningeal diseaseduring course of treatment and medication adjustment (FIG. 4A).Erlotinib, an EGFR tyrosine kinase inhibitor, was given orally at thedose of 150 mg daily and increased at time of progression to 600 mgevery 4 days and further to 900 mg (at 41 weeks) to achieve higherbrain/CSF concentration²⁹, followed by a prolonged remission. The levelsof both miR-10b and miR-200 members in CSF of this patient areconsistent with the MRI results, rising during relapse and returningback to background levels after the increase of erlotinib dosage(significant drop by 45 weeks, FIG. 4A).

Patient B (FIG. 4B) had GBM in remission at the initial cytological CSFanalysis and MRI that was interpreted as pseudoprogression. However,high levels of miR-10b, and significant elevation in miR-21 levels atlater time indicated disease progression that was further confirmed byMRI, PET scan and repeat biopsy of new lesion. Patient C (FIG. 4C) hadinadequate treatment due to functional status and rapidly progressedover a few weeks, which was reflected by an increase in levels ofmiR-200 family members.

Altogether, these data indicate for the first time that CSF miRNA levelsmay serve as biomarkers of brain cancer progression and response totherapy.

TABLE 3 miRNA Ct values 125b 10b 21 141 200a 200b 200c GBM 31.7864 UD29.3547 UD UD UD 39.7125 remission 31.9339 UD 29.1258 UD UD UD 39.1993GBM 33.5069 UD 32.0307 UD UD UD UD remission 33.8544 UD 32.6707 UD UD UDUD GBM 35.658 UD 34.5313 UD UD UD UD remission 35.5648 UD 36.6153 UD UDUD UD NSCLC 33.9462 UD 32.8533 UD UD UD UD remission 33.2768 UD 33.3858UD UD UD UD NSCLC 28.28 UD 27.57 UD UD UD UD remission 28.28 UD 27.57 UDUD UD UD NSCLC 35.02 UD 31.35 UD UD UD UD remission 35.02 UD 31.35 UD UDUD UD Breast 28.28 33.51 27.03 UD UD UD UD carcinoma 28.28 33.51 27.03UD UD UD UD remission

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OTHER EMBODIMENTS

It is to be understood that while the invention has been described inconjunction with the detailed description thereof, the foregoingdescription is intended to illustrate and not limit the scope of theinvention, which is defined by the scope of the appended claims. Otheraspects, advantages, and modifications are within the scope of thefollowing claims.

1. A method comprising: identifying a subject who has a brain tumor;providing a sample comprising cerebrospinal fluid (CSF) from the subjectwho has a brain tumor; and performing RT-PCR or deep RNA sequencing todetermine levels of miR-10b, miR-21, miR-125b, miR-141 miR-200a,miR-200b, and miR-200c in the sample from the subject.
 2. Acomputer-implemented method comprising: identifying a subject who has abrain tumor; providing a sample comprising cerebrospinal fluid (CSF)from the subject who has a brain tumor; and performing RT-PCR or deepRNA sequencing to determine levels of miR-10b, miR-21, miR-125b, miR-141miR-200a, miR-200b, and miR-200c, in the sample from the subject, toprovide a subject dataset; downloading the dataset into a computersystem having a memory, an output device, and a processor programmed forexecuting an algorithm, wherein the algorithm assigns the datasets intoone of two categories levels of miR-10b, miR-21, miR-125b, miR-141miR-200a, miR-200b, and miR-200c; assigning the subject dataset into thefirst or second category; and generating an output comprising a reportindicating the assignment to the first or second category.
 3. (canceled)4. (canceled)
 5. The method of claim 2, wherein the algorithm is alinear algorithm or radial basis function.
 6. The method of claim 2,wherein the algorithm is a linear algorithm comprising:(a*miR-125b)+(b*miR-10b)+(c*miR-21)+(d*miR-141)+(e*miR-200a)+(f*miR-200b)+(g*miR-200c)−h,wherein a-g are weights and h is a constant.
 7. The method of claim 2,wherein the first category is presence of a primary brain tumor or ametastatic brain tumor, and the second category is absence of a primarybrain tumor or a metastatic brain tumor, and the method furthercomprises selecting a treatment for a metastatic or primary brain tumorfor the subject, based on the presence of a metastatic or primary braintumor, wherein the treatment comprises administration of one or more ofsurgical resection, chemotherapy, or radiotherapy.
 8. The method ofclaim 7, further comprising administering the treatment to the subject.9. A method comprising: identifying a subject who has a brain tumor;providing a first sample comprising cerebrospinal fluid (CSF) from thesubject who has a brain tumor; determining levels of one or more ofmiR-10b, miR-21, miR-125b, miR-141 miR-200a, miR-200b, and miR-200c inthe first sample; providing a subsequent sample comprising cerebrospinalfluid (CSF) from the subject who has a brain tumor; and determininglevels of one or more of miR-10b, miR-21, miR-125b, miR-141 miR-200a,miR-200b, and miR-200c in the subsequent sample.
 10. (canceled)
 11. Themethod of claim 9, wherein the method further comprises administering atreatment to the subject, and wherein a decrease in levels of miR-10b,miR-21, miR-141 miR-200a, miR-200b, or miR-200c in the subsequent sampleas compared to the level in the first sample indicates that thetreatment is effective.
 12. The method of claim 11, wherein thetreatment comprises administration of one or more of surgical resection,chemotherapy, or radiotherapy.
 13. (canceled)
 14. (canceled)
 15. Themethod of claim 9, in which the levels are determined using RT-PCR ordeep RNA sequencing.
 16. (canceled)
 17. The method of claim 1, whereinthe method comprises normalizing the levels of miR-10b, miR-21,miR-125b, miR-141 miR-200a, miR-200b, and miR-200c to a level of miR-125or miR-24.
 18. (canceled)
 19. (canceled)
 20. The method of claim 6,wherein the algorithm is:0.3364*miR-125b+0.0808*miR-10b+0.4578*miR-21+−0.0871*miR-141+0.001*miR-200a+0.0213*miR-200b+−0.3419*miR-200c−7.2516.21. The method of claim 20, wherein a negative output indicates that thebrain tumor is glioblastoma (GBM), and a positive output indicates thatthe brain tumor is metastatic.
 22. The method of claim 6, wherein thealgorithm is:0.0003*miR-125b+−0.0021*miR-10b+−0.0002*miR-21+0*miR-141+0*miR-200a+0*miR-200b+−0.0021*miR-200c+3.1536.23. The method of claim 22, wherein a negative output indicates that thebrain tumor is glioblastoma (GBM) or metastatic cancer, and a positiveoutput indicates that the brain tumor is non-neoplastic.